Anthropic Claude Fable 5.2 用约 70 万次模拟笔触画出照片级画作

Anthropic's new language model Claude Fable 5.2 can create complex and even photorealistic paintings by using code to place hundreds of thousands of individual brushstrokes on a virtual canvas.

Current image models like Midjourney typically use diffusion, which gradually refines a noisy image into a clear result. Fable 5.2 takes a different approach by simulating the physical painting process, including bristle texture, how the paint goes on, and mistakes it then has to paint over.

Developer Jake Eaton showed on X what the approach can do. Eaton works at Anthropic, where he regularly contributes to research on language model values, safety mechanisms, and AI agent behavior.

When he asked Fable 5.2 to create a painting as indistinguishable from a photograph as possible, the model generated an image of a shopping cart in a parking garage using roughly 700,000 simulated brushstrokes.

According to Eaton, Claude works without an external image model and without pre-rendered or traced source images. Each piece is a Python program of 1,000 to 2,000 lines that drives a simulated paint engine. The approach resembles agentic image editing, where language models pick tools and evaluate results.

Earlier attempts with the sibling model Opus 5.5 drew criticism for merely imitating existing art. Eaton responded by having Fable 5.2 develop its own artist personas with fictional backstories and consistent styles. The model needs several thousand brushstrokes per painting, and Eaton redirects them with comments.

Without an undo button, the model has to commit to every stroke

Anthropic engineer Michael Graczyk shared a chat log and video of an experiment in which he asked Fable 5.2 to paint a bird at sunrise. The model could only use code to simulate the physics of bristles, wet paint, and canvas texture. It had to output every brushstroke manually as its own coordinate line, with no way to undo strokes once placed. That's what sets this method apart from AI-generated videos, where models like Flux 3 generate image sequences and their temporal consistency directly.

The resulting 21-second video uses a counter to track how the painting takes shape from exactly 111 strokes on a blank canvas. For the first stroke, the model output the color code #30346a, a width of 90 pixels, and five slightly wobbling waypoints. Fable 5.2 said it added the roughly seven-pixel wobble on purpose because a human arm doesn't draw a perfectly straight line either.

Between passes, the model looked at the canvas and reacted to its own mistakes like a real painter would. When the bird's back came out too thin and scraggly, the AI laid a bolder stroke over it, so the stray bristle marks along the edges now look like ruffled feathers.

In a second, 280-stroke piece showing two people painting at dusk, a late lighting effect accidentally smeared paint tubes and a cup on the table. Claude repainted those objects after the fact. The model also had both figures paint with their outer hands so their inner shoulders could touch, just as the prompt requested.

The same spatial reasoning transfers to physical robots

Fable 5.2's ability to plan step by step and self-correct under physical constraints isn't limited to virtual canvases. According to Anthropic, the model is built for extremely demanding long-horizon technical tasks, as is Mythos 5.2, a version designed for verified security and biology researchers.

In internal tests, Fable 5.2 solved two open problems in black hole physics, designed three working electronic devices with circuit board manufacturer Diode, and used more than 1,000 sub-agents to program its own laptop operating system in eight days.

AI researcher Aris Zhu tested how the model performs with real robotic arms. He pitted Fable 5.2 against OpenAI's competing model GPT-6 Astra, with each using a physical robot arm to paint Van Gogh's "The Starry Night." Anthropic reports a score of 64.9 percent for Fable 5.2 on its internal RoboDojo Sim benchmark, compared with 29.8 percent for GPT-6 Astra.

Zhu's real-world comparison still shows limits in fine motor control. Astra painted faster, and so far only OpenAI's model has managed to stand a domino on end. It did so by slowly loosening its gripper to use gravity as a pivot point.

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